diff options
author | Paul Baltescu <pauldb89@gmail.com> | 2013-11-23 17:33:47 +0000 |
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committer | Paul Baltescu <pauldb89@gmail.com> | 2013-11-23 17:33:47 +0000 |
commit | 072c4bb1edde483b87b93bc6f4eec36fc8a21008 (patch) | |
tree | 6ceaa6ae1e08df9e523282740b14f4857236297c /training | |
parent | 7e90b8ea10904f9b83f4e77e14c7396a3e6f7d5d (diff) | |
parent | 9e80389b9763aa4f7f626ec71b561ccf6948d3ad (diff) |
Merge branch 'master' of https://github.com/redpony/cdec
Diffstat (limited to 'training')
-rw-r--r-- | training/crf/mpi_online_optimize.cc | 8 | ||||
-rw-r--r-- | training/dtrain/Makefile.am | 2 | ||||
-rw-r--r-- | training/dtrain/README.md | 30 | ||||
-rw-r--r-- | training/dtrain/dtrain.cc | 201 | ||||
-rw-r--r-- | training/dtrain/dtrain.h | 2 | ||||
-rw-r--r-- | training/dtrain/examples/standard/dtrain.ini | 11 | ||||
-rw-r--r-- | training/dtrain/examples/standard/expected-output | 125 | ||||
-rw-r--r-- | training/dtrain/examples/standard/nc-wmt11.gz | bin | 0 -> 113504 bytes | |||
-rwxr-xr-x | training/dtrain/parallelize.rb | 20 | ||||
-rw-r--r-- | training/latent_svm/latent_svm.cc | 13 | ||||
-rw-r--r-- | training/mira/kbest_cut_mira.cc | 72 | ||||
-rw-r--r-- | training/mira/kbest_mira.cc | 18 | ||||
-rwxr-xr-x | training/mira/mira.py | 100 | ||||
-rw-r--r-- | training/pro/mr_pro_map.cc | 1 | ||||
-rw-r--r-- | training/utils/candidate_set.cc | 11 | ||||
-rw-r--r-- | training/utils/online_optimizer.h | 8 | ||||
-rw-r--r-- | training/utils/optimize_test.cc | 6 |
17 files changed, 395 insertions, 233 deletions
diff --git a/training/crf/mpi_online_optimize.cc b/training/crf/mpi_online_optimize.cc index 9e1ae34c..6b5b7d64 100644 --- a/training/crf/mpi_online_optimize.cc +++ b/training/crf/mpi_online_optimize.cc @@ -4,11 +4,11 @@ #include <vector> #include <cassert> #include <cmath> -#include <tr1/memory> #include <ctime> #include <boost/program_options.hpp> #include <boost/program_options/variables_map.hpp> +#include <boost/shared_ptr.hpp> #include "stringlib.h" #include "verbose.h" @@ -219,7 +219,7 @@ int main(int argc, char** argv) { #endif if (size > 1) SetSilent(true); // turn off verbose decoder output register_feature_functions(); - std::tr1::shared_ptr<MT19937> rng; + boost::shared_ptr<MT19937> rng; po::variables_map conf; if (!InitCommandLine(argc, argv, &conf)) @@ -264,8 +264,8 @@ int main(int argc, char** argv) { ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus, &ids); assert(corpus.size() > 0); - std::tr1::shared_ptr<OnlineOptimizer> o; - std::tr1::shared_ptr<LearningRateSchedule> lr; + boost::shared_ptr<OnlineOptimizer> o; + boost::shared_ptr<LearningRateSchedule> lr; const unsigned size_per_proc = conf["minibatch_size_per_proc"].as<unsigned>(); if (size_per_proc > corpus.size()) { diff --git a/training/dtrain/Makefile.am b/training/dtrain/Makefile.am index 844c790d..ecb6c128 100644 --- a/training/dtrain/Makefile.am +++ b/training/dtrain/Makefile.am @@ -1,7 +1,7 @@ bin_PROGRAMS = dtrain dtrain_SOURCES = dtrain.cc score.cc dtrain.h kbestget.h ksampler.h pairsampling.h score.h -dtrain_LDADD = ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mteval/libmteval.a ../../utils/libutils.a ../../klm/lm/libklm.a ../../klm/util/libklm_util.a ../../klm/util/double-conversion/libklm_util_double.a +dtrain_LDADD = ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mteval/libmteval.a ../../utils/libutils.a ../../klm/lm/libklm.a ../../klm/util/libklm_util.a ../../klm/util/double-conversion/libklm_util_double.a -lboost_regex AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval diff --git a/training/dtrain/README.md b/training/dtrain/README.md index 2bae6b48..aa1ab3e7 100644 --- a/training/dtrain/README.md +++ b/training/dtrain/README.md @@ -1,10 +1,15 @@ This is a simple (and parallelizable) tuning method for cdec -which is able to train the weights of very many (sparse) features. -It was used here: - "Joint Feature Selection in Distributed Stochastic - Learning for Large-Scale Discriminative Training in - SMT" -(Simianer, Riezler, Dyer; ACL 2012) +which is able to train the weights of very many (sparse) features +on the training set. + +It was used in these papers: +> "Joint Feature Selection in Distributed Stochastic +> Learning for Large-Scale Discriminative Training in +> SMT" (Simianer, Riezler, Dyer; ACL 2012) +> +> "Multi-Task Learning for Improved Discriminative +> Training in SMT" (Simianer, Riezler; WMT 2013) +> Building @@ -17,20 +22,9 @@ To build only parts needed for dtrain do cd training/dtrain/; make ``` -Ideas ------ - * get approx_bleu to work? - * implement minibatches (Minibatch and Parallelization for Online Large Margin Structured Learning) - * learning rate 1/T? - * use an oracle? mira-like (model vs. BLEU), feature repr. of reference!? - * implement lc_bleu properly - * merge kbest lists of previous epochs (as MERT does) - * ``walk entire regularization path'' - * rerank after each update? - Running ------- -See directories under test/ . +See directories under examples/ . Legal ----- diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc index 0ee2f124..0a27a068 100644 --- a/training/dtrain/dtrain.cc +++ b/training/dtrain/dtrain.cc @@ -12,8 +12,9 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) { po::options_description ini("Configuration File Options"); ini.add_options() - ("input", po::value<string>()->default_value("-"), "input file (src)") + ("input", po::value<string>(), "input file (src)") ("refs,r", po::value<string>(), "references") + ("bitext,b", po::value<string>(), "bitext: 'src ||| tgt'") ("output", po::value<string>()->default_value("-"), "output weights file, '-' for STDOUT") ("input_weights", po::value<string>(), "input weights file (e.g. from previous iteration)") ("decoder_config", po::value<string>(), "configuration file for cdec") @@ -40,6 +41,10 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) ("scale_bleu_diff", po::value<bool>()->zero_tokens(), "learning rate <- bleu diff of a misranked pair") ("loss_margin", po::value<weight_t>()->default_value(0.), "update if no error in pref pair but model scores this near") ("max_pairs", po::value<unsigned>()->default_value(std::numeric_limits<unsigned>::max()), "max. # of pairs per Sent.") + ("pclr", po::value<string>()->default_value("no"), "use a (simple|adagrad) per-coordinate learning rate") + ("batch", po::value<bool>()->zero_tokens(), "do batch optimization") + ("repeat", po::value<unsigned>()->default_value(1), "repeat optimization over kbest list this number of times") + //("test-k-best", po::value<bool>()->zero_tokens(), "check if optimization works (use repeat >= 2)") ("noup", po::value<bool>()->zero_tokens(), "do not update weights"); po::options_description cl("Command Line Options"); cl.add_options() @@ -72,13 +77,17 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) cerr << "Wrong 'pair_sampling' param: '" << (*cfg)["pair_sampling"].as<string>() << "'." << endl; return false; } - if(cfg->count("hi_lo") && (*cfg)["pair_sampling"].as<string>() != "XYX") { + if (cfg->count("hi_lo") && (*cfg)["pair_sampling"].as<string>() != "XYX") { cerr << "Warning: hi_lo only works with pair_sampling XYX." << endl; } - if((*cfg)["hi_lo"].as<float>() > 0.5 || (*cfg)["hi_lo"].as<float>() < 0.01) { + if ((*cfg)["hi_lo"].as<float>() > 0.5 || (*cfg)["hi_lo"].as<float>() < 0.01) { cerr << "hi_lo must lie in [0.01, 0.5]" << endl; return false; } + if ((cfg->count("input")>0 || cfg->count("refs")>0) && cfg->count("bitext")>0) { + cerr << "Provide 'input' and 'refs' or 'bitext', not both." << endl; + return false; + } if ((*cfg)["pair_threshold"].as<score_t>() < 0) { cerr << "The threshold must be >= 0!" << endl; return false; @@ -120,10 +129,16 @@ main(int argc, char** argv) const float hi_lo = cfg["hi_lo"].as<float>(); const score_t approx_bleu_d = cfg["approx_bleu_d"].as<score_t>(); const unsigned max_pairs = cfg["max_pairs"].as<unsigned>(); + int repeat = cfg["repeat"].as<unsigned>(); + //bool test_k_best = false; + //if (cfg.count("test-k-best")) test_k_best = true; weight_t loss_margin = cfg["loss_margin"].as<weight_t>(); + bool batch = false; + if (cfg.count("batch")) batch = true; if (loss_margin > 9998.) loss_margin = std::numeric_limits<float>::max(); bool scale_bleu_diff = false; if (cfg.count("scale_bleu_diff")) scale_bleu_diff = true; + const string pclr = cfg["pclr"].as<string>(); bool average = false; if (select_weights == "avg") average = true; @@ -131,7 +146,6 @@ main(int argc, char** argv) if (cfg.count("print_weights")) boost::split(print_weights, cfg["print_weights"].as<string>(), boost::is_any_of(" ")); - // setup decoder register_feature_functions(); SetSilent(true); @@ -178,17 +192,16 @@ main(int argc, char** argv) observer->SetScorer(scorer); // init weights - vector<weight_t>& dense_weights = decoder.CurrentWeightVector(); + vector<weight_t>& decoder_weights = decoder.CurrentWeightVector(); SparseVector<weight_t> lambdas, cumulative_penalties, w_average; - if (cfg.count("input_weights")) Weights::InitFromFile(cfg["input_weights"].as<string>(), &dense_weights); - Weights::InitSparseVector(dense_weights, &lambdas); + if (cfg.count("input_weights")) Weights::InitFromFile(cfg["input_weights"].as<string>(), &decoder_weights); + Weights::InitSparseVector(decoder_weights, &lambdas); // meta params for perceptron, SVM weight_t eta = cfg["learning_rate"].as<weight_t>(); weight_t gamma = cfg["gamma"].as<weight_t>(); // faster perceptron: consider only misranked pairs, see - // DO NOT ENABLE WITH SVM (gamma > 0) OR loss_margin! bool faster_perceptron = false; if (gamma==0 && loss_margin==0) faster_perceptron = true; @@ -208,13 +221,24 @@ main(int argc, char** argv) // output string output_fn = cfg["output"].as<string>(); // input - string input_fn = cfg["input"].as<string>(); + bool read_bitext = false; + string input_fn; + if (cfg.count("bitext")) { + read_bitext = true; + input_fn = cfg["bitext"].as<string>(); + } else { + input_fn = cfg["input"].as<string>(); + } ReadFile input(input_fn); // buffer input for t > 0 vector<string> src_str_buf; // source strings (decoder takes only strings) vector<vector<WordID> > ref_ids_buf; // references as WordID vecs - string refs_fn = cfg["refs"].as<string>(); - ReadFile refs(refs_fn); + ReadFile refs; + string refs_fn; + if (!read_bitext) { + refs_fn = cfg["refs"].as<string>(); + refs.Init(refs_fn); + } unsigned in_sz = std::numeric_limits<unsigned>::max(); // input index, input size vector<pair<score_t, score_t> > all_scores; @@ -229,6 +253,7 @@ main(int argc, char** argv) cerr << setw(25) << "k " << k << endl; cerr << setw(25) << "N " << N << endl; cerr << setw(25) << "T " << T << endl; + cerr << setw(25) << "batch " << batch << endl; cerr << setw(26) << "scorer '" << scorer_str << "'" << endl; if (scorer_str == "approx_bleu") cerr << setw(25) << "approx. B discount " << approx_bleu_d << endl; @@ -249,10 +274,14 @@ main(int argc, char** argv) cerr << setw(25) << "l1 reg " << l1_reg << " '" << cfg["l1_reg"].as<string>() << "'" << endl; if (rescale) cerr << setw(25) << "rescale " << rescale << endl; + cerr << setw(25) << "pclr " << pclr << endl; cerr << setw(25) << "max pairs " << max_pairs << endl; + cerr << setw(25) << "repeat " << repeat << endl; + //cerr << setw(25) << "test k-best " << test_k_best << endl; cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl; cerr << setw(25) << "input " << "'" << input_fn << "'" << endl; - cerr << setw(25) << "refs " << "'" << refs_fn << "'" << endl; + if (!read_bitext) + cerr << setw(25) << "refs " << "'" << refs_fn << "'" << endl; cerr << setw(25) << "output " << "'" << output_fn << "'" << endl; if (cfg.count("input_weights")) cerr << setw(25) << "weights in " << "'" << cfg["input_weights"].as<string>() << "'" << endl; @@ -261,6 +290,11 @@ main(int argc, char** argv) if (!verbose) cerr << "(a dot represents " << DTRAIN_DOTS << " inputs)" << endl; } + // pclr + SparseVector<weight_t> learning_rates; + // batch + SparseVector<weight_t> batch_updates; + score_t batch_loss; for (unsigned t = 0; t < T; t++) // T epochs { @@ -269,16 +303,24 @@ main(int argc, char** argv) time(&start); score_t score_sum = 0.; score_t model_sum(0); - unsigned ii = 0, rank_errors = 0, margin_violations = 0, npairs = 0, f_count = 0, list_sz = 0; + unsigned ii = 0, rank_errors = 0, margin_violations = 0, npairs = 0, f_count = 0, list_sz = 0, kbest_loss_improve = 0; + batch_loss = 0.; if (!quiet) cerr << "Iteration #" << t+1 << " of " << T << "." << endl; while(true) { string in; + string ref; bool next = false, stop = false; // next iteration or premature stop if (t == 0) { if(!getline(*input, in)) next = true; + if(read_bitext) { + vector<string> strs; + boost::algorithm::split_regex(strs, in, boost::regex(" \\|\\|\\| ")); + in = strs[0]; + ref = strs[1]; + } } else { if (ii == in_sz) next = true; // stop if we reach the end of our input } @@ -310,15 +352,16 @@ main(int argc, char** argv) if (next || stop) break; // weights - lambdas.init_vector(&dense_weights); + lambdas.init_vector(&decoder_weights); // getting input vector<WordID> ref_ids; // reference as vector<WordID> if (t == 0) { - string r_; - getline(*refs, r_); + if (!read_bitext) { + getline(*refs, ref); + } vector<string> ref_tok; - boost::split(ref_tok, r_, boost::is_any_of(" ")); + boost::split(ref_tok, ref, boost::is_any_of(" ")); register_and_convert(ref_tok, ref_ids); ref_ids_buf.push_back(ref_ids); src_str_buf.push_back(in); @@ -348,8 +391,10 @@ main(int argc, char** argv) } } - score_sum += (*samples)[0].score; // stats for 1best - model_sum += (*samples)[0].model; + if (repeat == 1) { + score_sum += (*samples)[0].score; // stats for 1best + model_sum += (*samples)[0].model; + } f_count += observer->get_f_count(); list_sz += observer->get_sz(); @@ -364,30 +409,74 @@ main(int argc, char** argv) partXYX(samples, pairs, pair_threshold, max_pairs, faster_perceptron, hi_lo); if (pair_sampling == "PRO") PROsampling(samples, pairs, pair_threshold, max_pairs); - npairs += pairs.size(); + int cur_npairs = pairs.size(); + npairs += cur_npairs; + + score_t kbest_loss_first, kbest_loss_last = 0.0; - SparseVector<weight_t> lambdas_copy; + for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin(); + it != pairs.end(); it++) { + score_t model_diff = it->first.model - it->second.model; + kbest_loss_first += max(0.0, -1.0 * model_diff); + } + + for (int ki=0; ki < repeat; ki++) { + + score_t kbest_loss = 0.0; // test-k-best + SparseVector<weight_t> lambdas_copy; // for l1 regularization + SparseVector<weight_t> sum_up; // for pclr if (l1naive||l1clip||l1cumul) lambdas_copy = lambdas; for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin(); it != pairs.end(); it++) { - bool rank_error; + score_t model_diff = it->first.model - it->second.model; + if (repeat > 1) { + model_diff = lambdas.dot(it->first.f) - lambdas.dot(it->second.f); + kbest_loss += max(0.0, -1.0 * model_diff); + } + bool rank_error = false; score_t margin; if (faster_perceptron) { // we only have considering misranked pairs rank_error = true; // pair sampling already did this for us margin = std::numeric_limits<float>::max(); } else { - rank_error = it->first.model <= it->second.model; - margin = fabs(it->first.model - it->second.model); + rank_error = model_diff<=0.0; + margin = fabs(model_diff); if (!rank_error && margin < loss_margin) margin_violations++; } - if (rank_error) rank_errors++; + if (rank_error && ki==1) rank_errors++; if (scale_bleu_diff) eta = it->first.score - it->second.score; if (rank_error || margin < loss_margin) { SparseVector<weight_t> diff_vec = it->first.f - it->second.f; - lambdas.plus_eq_v_times_s(diff_vec, eta); - if (gamma) - lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs)); + if (batch) { + batch_loss += max(0., -1.0*model_diff); + batch_updates += diff_vec; + continue; + } + if (pclr != "no") { + sum_up += diff_vec; + } else { + lambdas.plus_eq_v_times_s(diff_vec, eta); + if (gamma) lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./cur_npairs)); + } + } + } + + // per-coordinate learning rate + if (pclr != "no") { + SparseVector<weight_t>::iterator it = sum_up.begin(); + for (; it != sum_up.end(); ++it) { + if (pclr == "simple") { + lambdas[it->first] += it->second / max(1.0, learning_rates[it->first]); + learning_rates[it->first]++; + } else if (pclr == "adagrad") { + if (learning_rates[it->first] == 0) { + lambdas[it->first] += it->second * eta; + } else { + lambdas[it->first] += it->second * eta * learning_rates[it->first]; + } + learning_rates[it->first] += pow(it->second, 2.0); + } } } @@ -395,14 +484,16 @@ main(int argc, char** argv) // please note that this regularizations happen // after a _sentence_ -- not after each example/pair! if (l1naive) { - FastSparseVector<weight_t>::iterator it = lambdas.begin(); + SparseVector<weight_t>::iterator it = lambdas.begin(); for (; it != lambdas.end(); ++it) { if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) { + it->second *= max(0.0000001, eta/(eta+learning_rates[it->first])); // FIXME + learning_rates[it->first]++; it->second -= sign(it->second) * l1_reg; } } } else if (l1clip) { - FastSparseVector<weight_t>::iterator it = lambdas.begin(); + SparseVector<weight_t>::iterator it = lambdas.begin(); for (; it != lambdas.end(); ++it) { if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) { if (it->second != 0) { @@ -417,7 +508,7 @@ main(int argc, char** argv) } } else if (l1cumul) { weight_t acc_penalty = (ii+1) * l1_reg; // ii is the index of the current input - FastSparseVector<weight_t>::iterator it = lambdas.begin(); + SparseVector<weight_t>::iterator it = lambdas.begin(); for (; it != lambdas.end(); ++it) { if (!lambdas_copy.get(it->first) || lambdas_copy.get(it->first)!=it->second) { if (it->second != 0) { @@ -435,7 +526,28 @@ main(int argc, char** argv) } } - } + if (ki==repeat-1) { // done + kbest_loss_last = kbest_loss; + if (repeat > 1) { + score_t best_score = -1.; + score_t best_model = -std::numeric_limits<score_t>::max(); + unsigned best_idx; + for (unsigned i=0; i < samples->size(); i++) { + score_t s = lambdas.dot((*samples)[i].f); + if (s > best_model) { + best_idx = i; + best_model = s; + } + } + score_sum += (*samples)[best_idx].score; + model_sum += best_model; + } + } + } // repeat + + if ((kbest_loss_first - kbest_loss_last) >= 0) kbest_loss_improve++; + + } // noup if (rescale) lambdas /= lambdas.l2norm(); @@ -443,14 +555,19 @@ main(int argc, char** argv) } // input loop - if (average) w_average += lambdas; + if (t == 0) in_sz = ii; // remember size of input (# lines) - if (scorer_str == "approx_bleu" || scorer_str == "lc_bleu") scorer->Reset(); - if (t == 0) { - in_sz = ii; // remember size of input (# lines) + if (batch) { + lambdas.plus_eq_v_times_s(batch_updates, eta); + if (gamma) lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs)); + batch_updates.clear(); } + if (average) w_average += lambdas; + + if (scorer_str == "approx_bleu" || scorer_str == "lc_bleu") scorer->Reset(); + // print some stats score_t score_avg = score_sum/(score_t)in_sz; score_t model_avg = model_sum/(score_t)in_sz; @@ -477,13 +594,15 @@ main(int argc, char** argv) cerr << _np << " 1best avg model score: " << model_avg; cerr << _p << " (" << model_diff << ")" << endl; cerr << " avg # pairs: "; - cerr << _np << npairs/(float)in_sz; + cerr << _np << npairs/(float)in_sz << endl; + cerr << " avg # rank err: "; + cerr << rank_errors/(float)in_sz; if (faster_perceptron) cerr << " (meaningless)"; cerr << endl; - cerr << " avg # rank err: "; - cerr << rank_errors/(float)in_sz << endl; cerr << " avg # margin viol: "; cerr << margin_violations/(float)in_sz << endl; + if (batch) cerr << " batch loss: " << batch_loss << endl; + cerr << " k-best loss imp: " << ((float)kbest_loss_improve/in_sz)*100 << "%" << endl; cerr << " non0 feature count: " << nonz << endl; cerr << " avg list sz: " << list_sz/(float)in_sz << endl; cerr << " avg f count: " << f_count/(float)list_sz << endl; @@ -510,9 +629,9 @@ main(int argc, char** argv) // write weights to file if (select_weights == "best" || keep) { - lambdas.init_vector(&dense_weights); + lambdas.init_vector(&decoder_weights); string w_fn = "weights." + boost::lexical_cast<string>(t) + ".gz"; - Weights::WriteToFile(w_fn, dense_weights, true); + Weights::WriteToFile(w_fn, decoder_weights, true); } } // outer loop diff --git a/training/dtrain/dtrain.h b/training/dtrain/dtrain.h index 3981fb39..ccb5ad4d 100644 --- a/training/dtrain/dtrain.h +++ b/training/dtrain/dtrain.h @@ -9,6 +9,8 @@ #include <string.h> #include <boost/algorithm/string.hpp> +#include <boost/regex.hpp> +#include <boost/algorithm/string/regex.hpp> #include <boost/program_options.hpp> #include "decoder.h" diff --git a/training/dtrain/examples/standard/dtrain.ini b/training/dtrain/examples/standard/dtrain.ini index 23e94285..fc83f08e 100644 --- a/training/dtrain/examples/standard/dtrain.ini +++ b/training/dtrain/examples/standard/dtrain.ini @@ -1,5 +1,6 @@ -input=./nc-wmt11.de.gz -refs=./nc-wmt11.en.gz +#input=./nc-wmt11.de.gz +#refs=./nc-wmt11.en.gz +bitext=./nc-wmt11.gz output=- # a weights file (add .gz for gzip compression) or STDOUT '-' select_weights=VOID # output average (over epochs) weight vector decoder_config=./cdec.ini # config for cdec @@ -10,11 +11,11 @@ print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 Phr stop_after=10 # stop epoch after 10 inputs # interesting stuff -epochs=2 # run over input 2 times +epochs=3 # run over input 3 times k=100 # use 100best lists N=4 # optimize (approx) BLEU4 scorer=fixed_stupid_bleu # use 'stupid' BLEU+1 -learning_rate=1.0 # learning rate, don't care if gamma=0 (perceptron) +learning_rate=0.1 # learning rate, don't care if gamma=0 (perceptron) and loss_margin=0 (not margin perceptron) gamma=0 # use SVM reg sample_from=kbest # use kbest lists (as opposed to forest) filter=uniq # only unique entries in kbest (surface form) @@ -22,3 +23,5 @@ pair_sampling=XYX # hi_lo=0.1 # 10 vs 80 vs 10 and 80 vs 10 here pair_threshold=0 # minimum distance in BLEU (here: > 0) loss_margin=0 # update if correctly ranked, but within this margin +repeat=1 # repeat training on a kbest list 1 times +#batch=true # batch tuning, update after accumulating over all sentences and all kbest lists diff --git a/training/dtrain/examples/standard/expected-output b/training/dtrain/examples/standard/expected-output index 21f91244..75f47337 100644 --- a/training/dtrain/examples/standard/expected-output +++ b/training/dtrain/examples/standard/expected-output @@ -4,17 +4,18 @@ Reading ./nc-wmt11.en.srilm.gz ----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100 **************************************************************************************************** Example feature: Shape_S00000_T00000 -Seeding random number sequence to 970626287 +Seeding random number sequence to 3751911392 dtrain Parameters: k 100 N 4 - T 2 + T 3 + batch 0 scorer 'fixed_stupid_bleu' sample from 'kbest' filter 'uniq' - learning rate 1 + learning rate 0.1 gamma 0 loss margin 0 faster perceptron 1 @@ -23,69 +24,99 @@ Parameters: pair threshold 0 select weights 'VOID' l1 reg 0 'none' + pclr no max pairs 4294967295 + repeat 1 cdec cfg './cdec.ini' - input './nc-wmt11.de.gz' - refs './nc-wmt11.en.gz' + input './nc-wmt11.gz' output '-' stop_after 10 (a dot represents 10 inputs) -Iteration #1 of 2. +Iteration #1 of 3. . 10 Stopping after 10 input sentences. WEIGHTS - Glue = -614 - WordPenalty = +1256.8 - LanguageModel = +5610.5 - LanguageModel_OOV = -1449 - PhraseModel_0 = -2107 - PhraseModel_1 = -4666.1 - PhraseModel_2 = -2713.5 - PhraseModel_3 = +4204.3 - PhraseModel_4 = -1435.8 - PhraseModel_5 = +916 - PhraseModel_6 = +190 - PassThrough = -2527 + Glue = -110 + WordPenalty = -8.2082 + LanguageModel = -319.91 + LanguageModel_OOV = -19.2 + PhraseModel_0 = +312.82 + PhraseModel_1 = -161.02 + PhraseModel_2 = -433.65 + PhraseModel_3 = +291.03 + PhraseModel_4 = +252.32 + PhraseModel_5 = +50.6 + PhraseModel_6 = +146.7 + PassThrough = -38.7 --- - 1best avg score: 0.17874 (+0.17874) - 1best avg model score: 88399 (+88399) - avg # pairs: 798.2 (meaningless) - avg # rank err: 798.2 + 1best avg score: 0.16966 (+0.16966) + 1best avg model score: 29874 (+29874) + avg # pairs: 906.3 + avg # rank err: 0 (meaningless) avg # margin viol: 0 - non0 feature count: 887 + k-best loss imp: 100% + non0 feature count: 832 avg list sz: 91.3 - avg f count: 126.85 -(time 0.33 min, 2 s/S) + avg f count: 139.77 +(time 0.35 min, 2.1 s/S) -Iteration #2 of 2. +Iteration #2 of 3. . 10 WEIGHTS - Glue = -1025 - WordPenalty = +1751.5 - LanguageModel = +10059 - LanguageModel_OOV = -4490 - PhraseModel_0 = -2640.7 - PhraseModel_1 = -3757.4 - PhraseModel_2 = -1133.1 - PhraseModel_3 = +1837.3 - PhraseModel_4 = -3534.3 - PhraseModel_5 = +2308 - PhraseModel_6 = +1677 - PassThrough = -6222 + Glue = -122.1 + WordPenalty = +83.689 + LanguageModel = +233.23 + LanguageModel_OOV = -145.1 + PhraseModel_0 = +150.72 + PhraseModel_1 = -272.84 + PhraseModel_2 = -418.36 + PhraseModel_3 = +181.63 + PhraseModel_4 = -289.47 + PhraseModel_5 = +140.3 + PhraseModel_6 = +3.5 + PassThrough = -109.7 --- - 1best avg score: 0.30764 (+0.12891) - 1best avg model score: -2.5042e+05 (-3.3882e+05) - avg # pairs: 725.9 (meaningless) - avg # rank err: 725.9 + 1best avg score: 0.17399 (+0.004325) + 1best avg model score: 4936.9 (-24937) + avg # pairs: 662.4 + avg # rank err: 0 (meaningless) avg # margin viol: 0 - non0 feature count: 1499 + k-best loss imp: 100% + non0 feature count: 1240 avg list sz: 91.3 - avg f count: 114.34 -(time 0.32 min, 1.9 s/S) + avg f count: 125.11 +(time 0.27 min, 1.6 s/S) + +Iteration #3 of 3. + . 10 +WEIGHTS + Glue = -157.4 + WordPenalty = -1.7372 + LanguageModel = +686.18 + LanguageModel_OOV = -399.7 + PhraseModel_0 = -39.876 + PhraseModel_1 = -341.96 + PhraseModel_2 = -318.67 + PhraseModel_3 = +105.08 + PhraseModel_4 = -290.27 + PhraseModel_5 = -48.6 + PhraseModel_6 = -43.6 + PassThrough = -298.5 + --- + 1best avg score: 0.30742 (+0.13343) + 1best avg model score: -15393 (-20329) + avg # pairs: 623.8 + avg # rank err: 0 (meaningless) + avg # margin viol: 0 + k-best loss imp: 100% + non0 feature count: 1776 + avg list sz: 91.3 + avg f count: 118.58 +(time 0.28 min, 1.7 s/S) Writing weights file to '-' ... done --- -Best iteration: 2 [SCORE 'fixed_stupid_bleu'=0.30764]. -This took 0.65 min. +Best iteration: 3 [SCORE 'fixed_stupid_bleu'=0.30742]. +This took 0.9 min. diff --git a/training/dtrain/examples/standard/nc-wmt11.gz b/training/dtrain/examples/standard/nc-wmt11.gz Binary files differnew file mode 100644 index 00000000..c39c5aef --- /dev/null +++ b/training/dtrain/examples/standard/nc-wmt11.gz diff --git a/training/dtrain/parallelize.rb b/training/dtrain/parallelize.rb index 285f3c9b..60ca9422 100755 --- a/training/dtrain/parallelize.rb +++ b/training/dtrain/parallelize.rb @@ -21,6 +21,8 @@ opts = Trollop::options do opt :qsub, "use qsub", :type => :bool, :default => false opt :dtrain_binary, "path to dtrain binary", :type => :string opt :extra_qsub, "extra qsub args", :type => :string, :default => "" + opt :per_shard_decoder_configs, "give special decoder config per shard", :type => :string, :short => '-o' + opt :first_input_weights, "input weights for first iter", :type => :string, :default => '', :short => '-w' end usage if not opts[:config]&&opts[:shards]&&opts[:input]&&opts[:references] @@ -41,9 +43,11 @@ epochs = opts[:epochs] rand = opts[:randomize] reshard = opts[:reshard] predefined_shards = false +per_shard_decoder_configs = false if opts[:shards] == 0 predefined_shards = true num_shards = 0 + per_shard_decoder_configs = true if opts[:per_shard_decoder_configs] else num_shards = opts[:shards] end @@ -51,6 +55,7 @@ input = opts[:input] refs = opts[:references] use_qsub = opts[:qsub] shards_at_once = opts[:processes_at_once] +first_input_weights = opts[:first_input_weights] `mkdir work` @@ -101,6 +106,9 @@ refs_files = [] if predefined_shards input_files = File.new(input).readlines.map {|i| i.strip } refs_files = File.new(refs).readlines.map {|i| i.strip } + if per_shard_decoder_configs + decoder_configs = File.new(opts[:per_shard_decoder_configs]).readlines.map {|i| i.strip} + end num_shards = input_files.size else input_files, refs_files = make_shards input, refs, num_shards, 0, rand @@ -126,10 +134,18 @@ end else local_end = "2>work/out.#{shard}.#{epoch}" end + if per_shard_decoder_configs + cdec_cfg = "--decoder_config #{decoder_configs[shard]}" + else + cdec_cfg = "" + end + if first_input_weights!='' && epoch == 0 + input_weights = "--input_weights #{first_input_weights}" + end pids << Kernel.fork { - `#{qsub_str_start}#{dtrain_bin} -c #{ini}\ + `#{qsub_str_start}#{dtrain_bin} -c #{ini} #{cdec_cfg} #{input_weights}\ --input #{input_files[shard]}\ - --refs #{refs_files[shard]} #{input_weights}\ + --refs #{refs_files[shard]}\ --output work/weights.#{shard}.#{epoch}#{qsub_str_end} #{local_end}` } weights_files << "work/weights.#{shard}.#{epoch}" diff --git a/training/latent_svm/latent_svm.cc b/training/latent_svm/latent_svm.cc index ab9c1d5d..60e52550 100644 --- a/training/latent_svm/latent_svm.cc +++ b/training/latent_svm/latent_svm.cc @@ -32,7 +32,6 @@ total_loss and prev_loss actually refer not to loss, but the metric (usually BLE #include "sampler.h" using namespace std; -using boost::shared_ptr; namespace po = boost::program_options; bool invert_score; @@ -128,7 +127,7 @@ struct HypothesisInfo { }; struct GoodOracle { - shared_ptr<HypothesisInfo> good; + boost::shared_ptr<HypothesisInfo> good; }; struct TrainingObserver : public DecoderObserver { @@ -143,9 +142,9 @@ struct TrainingObserver : public DecoderObserver { const DocScorer& ds; const vector<weight_t>& feature_weights; vector<GoodOracle>& oracles; - shared_ptr<HypothesisInfo> cur_best; - shared_ptr<HypothesisInfo> cur_costaug_best; - shared_ptr<HypothesisInfo> cur_ref; + boost::shared_ptr<HypothesisInfo> cur_best; + boost::shared_ptr<HypothesisInfo> cur_costaug_best; + boost::shared_ptr<HypothesisInfo> cur_ref; const int kbest_size; const double mt_metric_scale; const double mu; @@ -168,8 +167,8 @@ struct TrainingObserver : public DecoderObserver { UpdateOracles(smeta.GetSentenceID(), *hg); } - shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double metric) { - shared_ptr<HypothesisInfo> h(new HypothesisInfo); + boost::shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double metric) { + boost::shared_ptr<HypothesisInfo> h(new HypothesisInfo); h->features = feats; h->mt_metric_score = metric; return h; diff --git a/training/mira/kbest_cut_mira.cc b/training/mira/kbest_cut_mira.cc index 59fa860a..990609d7 100644 --- a/training/mira/kbest_cut_mira.cc +++ b/training/mira/kbest_cut_mira.cc @@ -30,7 +30,6 @@ #include "sparse_vector.h" using namespace std; -using boost::shared_ptr; namespace po = boost::program_options; bool invert_score; @@ -50,13 +49,6 @@ bool sent_approx; bool checkloss; bool stream; -void SanityCheck(const vector<double>& w) { - for (int i = 0; i < w.size(); ++i) { - assert(!isnan(w[i])); - assert(!isinf(w[i])); - } -} - struct FComp { const vector<double>& w_; FComp(const vector<double>& w) : w_(w) {} @@ -149,7 +141,7 @@ struct HypothesisInfo { double alpha; double oracle_loss; SparseVector<double> oracle_feat_diff; - shared_ptr<HypothesisInfo> oracleN; + boost::shared_ptr<HypothesisInfo> oracleN; }; bool ApproxEqual(double a, double b) { @@ -157,7 +149,7 @@ bool ApproxEqual(double a, double b) { return (fabs(a-b)/fabs(b)) < EPSILON; } -typedef shared_ptr<HypothesisInfo> HI; +typedef boost::shared_ptr<HypothesisInfo> HI; bool HypothesisCompareB(const HI& h1, const HI& h2 ) { return h1->mt_metric > h2->mt_metric; @@ -185,11 +177,11 @@ bool HypothesisCompareG(const HI& h1, const HI& h2 ) }; -void CuttingPlane(vector<shared_ptr<HypothesisInfo> >* cur_c, bool* again, vector<shared_ptr<HypothesisInfo> >& all_hyp, vector<weight_t> dense_weights) +void CuttingPlane(vector<boost::shared_ptr<HypothesisInfo> >* cur_c, bool* again, vector<boost::shared_ptr<HypothesisInfo> >& all_hyp, vector<weight_t> dense_weights) { bool DEBUG_CUT = false; - shared_ptr<HypothesisInfo> max_fear, max_fear_in_set; - vector<shared_ptr<HypothesisInfo> >& cur_constraint = *cur_c; + boost::shared_ptr<HypothesisInfo> max_fear, max_fear_in_set; + vector<boost::shared_ptr<HypothesisInfo> >& cur_constraint = *cur_c; if(no_reweight) { @@ -235,9 +227,9 @@ void CuttingPlane(vector<shared_ptr<HypothesisInfo> >* cur_c, bool* again, vecto } -double ComputeDelta(vector<shared_ptr<HypothesisInfo> >* cur_p, double max_step_size,vector<weight_t> dense_weights ) +double ComputeDelta(vector<boost::shared_ptr<HypothesisInfo> >* cur_p, double max_step_size,vector<weight_t> dense_weights ) { - vector<shared_ptr<HypothesisInfo> >& cur_pair = *cur_p; + vector<boost::shared_ptr<HypothesisInfo> >& cur_pair = *cur_p; double loss = cur_pair[0]->oracle_loss - cur_pair[1]->oracle_loss; double margin = -(cur_pair[0]->oracleN->features.dot(dense_weights)- cur_pair[0]->features.dot(dense_weights)) + (cur_pair[1]->oracleN->features.dot(dense_weights) - cur_pair[1]->features.dot(dense_weights)); @@ -261,12 +253,12 @@ double ComputeDelta(vector<shared_ptr<HypothesisInfo> >* cur_p, double max_step_ } -vector<shared_ptr<HypothesisInfo> > SelectPair(vector<shared_ptr<HypothesisInfo> >* cur_c) +vector<boost::shared_ptr<HypothesisInfo> > SelectPair(vector<boost::shared_ptr<HypothesisInfo> >* cur_c) { bool DEBUG_SELECT= false; - vector<shared_ptr<HypothesisInfo> >& cur_constraint = *cur_c; + vector<boost::shared_ptr<HypothesisInfo> >& cur_constraint = *cur_c; - vector<shared_ptr<HypothesisInfo> > pair; + vector<boost::shared_ptr<HypothesisInfo> > pair; if (no_select || optimizer == 2){ //skip heuristic search and return oracle and fear for pa-mira @@ -278,7 +270,7 @@ vector<shared_ptr<HypothesisInfo> > SelectPair(vector<shared_ptr<HypothesisInfo> for(int u=0;u != cur_constraint.size();u++) { - shared_ptr<HypothesisInfo> max_fear; + boost::shared_ptr<HypothesisInfo> max_fear; if(DEBUG_SELECT) cerr<< "cur alpha " << u << " " << cur_constraint[u]->alpha; for(int i=0; i < cur_constraint.size();i++) //select maximal violator @@ -323,8 +315,8 @@ vector<shared_ptr<HypothesisInfo> > SelectPair(vector<shared_ptr<HypothesisInfo> } struct GoodBadOracle { - vector<shared_ptr<HypothesisInfo> > good; - vector<shared_ptr<HypothesisInfo> > bad; + vector<boost::shared_ptr<HypothesisInfo> > good; + vector<boost::shared_ptr<HypothesisInfo> > bad; }; struct BasicObserver: public DecoderObserver { @@ -367,8 +359,8 @@ struct TrainingObserver : public DecoderObserver { const DocScorer& ds; vector<ScoreP>& corpus_bleu_sent_stats; vector<GoodBadOracle>& oracles; - vector<shared_ptr<HypothesisInfo> > cur_best; - shared_ptr<HypothesisInfo> cur_oracle; + vector<boost::shared_ptr<HypothesisInfo> > cur_best; + boost::shared_ptr<HypothesisInfo> cur_oracle; const int kbest_size; Hypergraph forest; int cur_sent; @@ -386,7 +378,7 @@ struct TrainingObserver : public DecoderObserver { return *cur_best[0]; } - const vector<shared_ptr<HypothesisInfo> > GetCurrentBest() const { + const vector<boost::shared_ptr<HypothesisInfo> > GetCurrentBest() const { return cur_best; } @@ -411,8 +403,8 @@ struct TrainingObserver : public DecoderObserver { } - shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double score, const vector<WordID>& hyp) { - shared_ptr<HypothesisInfo> h(new HypothesisInfo); + boost::shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double score, const vector<WordID>& hyp) { + boost::shared_ptr<HypothesisInfo> h(new HypothesisInfo); h->features = feats; h->mt_metric = score; h->hyp = hyp; @@ -424,14 +416,14 @@ struct TrainingObserver : public DecoderObserver { if (stream) sent_id = 0; bool PRINT_LIST= false; - vector<shared_ptr<HypothesisInfo> >& cur_good = oracles[sent_id].good; - vector<shared_ptr<HypothesisInfo> >& cur_bad = oracles[sent_id].bad; + vector<boost::shared_ptr<HypothesisInfo> >& cur_good = oracles[sent_id].good; + vector<boost::shared_ptr<HypothesisInfo> >& cur_bad = oracles[sent_id].bad; //TODO: look at keeping previous iterations hypothesis lists around cur_best.clear(); cur_good.clear(); cur_bad.clear(); - vector<shared_ptr<HypothesisInfo> > all_hyp; + vector<boost::shared_ptr<HypothesisInfo> > all_hyp; typedef KBest::KBestDerivations<vector<WordID>, ESentenceTraversal,Filter> K; K kbest(forest,kbest_size); @@ -527,7 +519,7 @@ struct TrainingObserver : public DecoderObserver { if(PRINT_LIST) { cerr << "GOOD" << endl; for(int u=0;u!=cur_good.size();u++) cerr << cur_good[u]->mt_metric << " " << cur_good[u]->hope << endl;} //use hope for fear selection - shared_ptr<HypothesisInfo>& oracleN = cur_good[0]; + boost::shared_ptr<HypothesisInfo>& oracleN = cur_good[0]; if(fear_select == 1){ //compute fear hyps with model - bleu if (PRINT_LIST) cerr << "FEAR " << endl; @@ -663,13 +655,13 @@ int main(int argc, char** argv) { invert_score = false; } - shared_ptr<DocScorer> ds; + boost::shared_ptr<DocScorer> ds; //normal: load references, stream: start stream scorer if (stream) { - ds = shared_ptr<DocScorer>(new DocStreamScorer(type, vector<string>(0), "")); + ds = boost::shared_ptr<DocScorer>(new DocStreamScorer(type, vector<string>(0), "")); cerr << "Scoring doc stream with " << metric_name << endl; } else { - ds = shared_ptr<DocScorer>(new DocScorer(type, conf["reference"].as<vector<string> >(), "")); + ds = boost::shared_ptr<DocScorer>(new DocScorer(type, conf["reference"].as<vector<string> >(), "")); cerr << "Loaded " << ds->size() << " references for scoring with " << metric_name << endl; } vector<ScoreP> corpus_bleu_sent_stats; @@ -774,9 +766,9 @@ int main(int argc, char** argv) { const HypothesisInfo& cur_good = *oracles[cur_sent].good[0]; const HypothesisInfo& cur_bad = *oracles[cur_sent].bad[0]; - vector<shared_ptr<HypothesisInfo> >& cur_good_v = oracles[cur_sent].good; - vector<shared_ptr<HypothesisInfo> >& cur_bad_v = oracles[cur_sent].bad; - vector<shared_ptr<HypothesisInfo> > cur_best_v = observer.GetCurrentBest(); + vector<boost::shared_ptr<HypothesisInfo> >& cur_good_v = oracles[cur_sent].good; + vector<boost::shared_ptr<HypothesisInfo> >& cur_bad_v = oracles[cur_sent].bad; + vector<boost::shared_ptr<HypothesisInfo> > cur_best_v = observer.GetCurrentBest(); tot_loss += cur_hyp.mt_metric; @@ -824,13 +816,13 @@ int main(int argc, char** argv) { } else if(optimizer == 5) //full mira with n-best list of constraints from hope, fear, model best { - vector<shared_ptr<HypothesisInfo> > cur_constraint; + vector<boost::shared_ptr<HypothesisInfo> > cur_constraint; cur_constraint.insert(cur_constraint.begin(), cur_bad_v.begin(), cur_bad_v.end()); cur_constraint.insert(cur_constraint.begin(), cur_best_v.begin(), cur_best_v.end()); cur_constraint.insert(cur_constraint.begin(), cur_good_v.begin(), cur_good_v.end()); bool optimize_again; - vector<shared_ptr<HypothesisInfo> > cur_pair; + vector<boost::shared_ptr<HypothesisInfo> > cur_pair; //SMO for(int u=0;u!=cur_constraint.size();u++) cur_constraint[u]->alpha =0; @@ -879,7 +871,7 @@ int main(int argc, char** argv) { else if(optimizer == 2 || optimizer == 3) //PA and Cutting Plane MIRA update { bool DEBUG_SMO= true; - vector<shared_ptr<HypothesisInfo> > cur_constraint; + vector<boost::shared_ptr<HypothesisInfo> > cur_constraint; cur_constraint.push_back(cur_good_v[0]); //add oracle to constraint set bool optimize_again = true; int cut_plane_calls = 0; @@ -919,7 +911,7 @@ int main(int argc, char** argv) { while (iter < smo_iter) { //select pair to optimize from constraint set - vector<shared_ptr<HypothesisInfo> > cur_pair = SelectPair(&cur_constraint); + vector<boost::shared_ptr<HypothesisInfo> > cur_pair = SelectPair(&cur_constraint); if(cur_pair.empty()){ iter=MAX_SMO; diff --git a/training/mira/kbest_mira.cc b/training/mira/kbest_mira.cc index d59b4224..2868de0c 100644 --- a/training/mira/kbest_mira.cc +++ b/training/mira/kbest_mira.cc @@ -3,10 +3,10 @@ #include <vector> #include <cassert> #include <cmath> -#include <tr1/memory> #include <boost/program_options.hpp> #include <boost/program_options/variables_map.hpp> +#include <boost/shared_ptr.hpp> #include "stringlib.h" #include "hg_sampler.h" @@ -30,7 +30,7 @@ using namespace std; namespace po = boost::program_options; bool invert_score; -std::tr1::shared_ptr<MT19937> rng; +boost::shared_ptr<MT19937> rng; void RandomPermutation(int len, vector<int>* p_ids) { vector<int>& ids = *p_ids; @@ -88,8 +88,8 @@ struct HypothesisInfo { }; struct GoodBadOracle { - std::tr1::shared_ptr<HypothesisInfo> good; - std::tr1::shared_ptr<HypothesisInfo> bad; + boost::shared_ptr<HypothesisInfo> good; + boost::shared_ptr<HypothesisInfo> bad; }; struct TrainingObserver : public DecoderObserver { @@ -97,7 +97,7 @@ struct TrainingObserver : public DecoderObserver { const DocumentScorer& ds; const EvaluationMetric& metric; vector<GoodBadOracle>& oracles; - std::tr1::shared_ptr<HypothesisInfo> cur_best; + boost::shared_ptr<HypothesisInfo> cur_best; const int kbest_size; const bool sample_forest; @@ -109,16 +109,16 @@ struct TrainingObserver : public DecoderObserver { UpdateOracles(smeta.GetSentenceID(), *hg); } - std::tr1::shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double score) { - std::tr1::shared_ptr<HypothesisInfo> h(new HypothesisInfo); + boost::shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double score) { + boost::shared_ptr<HypothesisInfo> h(new HypothesisInfo); h->features = feats; h->mt_metric = score; return h; } void UpdateOracles(int sent_id, const Hypergraph& forest) { - std::tr1::shared_ptr<HypothesisInfo>& cur_good = oracles[sent_id].good; - std::tr1::shared_ptr<HypothesisInfo>& cur_bad = oracles[sent_id].bad; + boost::shared_ptr<HypothesisInfo>& cur_good = oracles[sent_id].good; + boost::shared_ptr<HypothesisInfo>& cur_bad = oracles[sent_id].bad; cur_bad.reset(); // TODO get rid of?? if (sample_forest) { diff --git a/training/mira/mira.py b/training/mira/mira.py index 29c51e1d..d5a1d9f8 100755 --- a/training/mira/mira.py +++ b/training/mira/mira.py @@ -4,8 +4,19 @@ import subprocess, shlex, glob import argparse import logging import random, time -import cdec.score import gzip, itertools +try: + import cdec.score +except ImportError: + sys.stderr.write('Could not import pycdec, see cdec/python/README.md for details\n') + sys.exit(1) +have_mpl = True +try: + import matplotlib + matplotlib.use('Agg') + import matplotlib.pyplot as plt +except ImportError: + have_mpl = False #mira run script #requires pycdec to be built, since it is used for scoring hypothesis @@ -16,17 +27,17 @@ import gzip, itertools #scoring function using pycdec scoring def fast_score(hyps, refs, metric): scorer = cdec.score.Scorer(metric) - logging.info('loaded {0} references for scoring with {1}\n'.format( + logging.info('loaded {0} references for scoring with {1}'.format( len(refs), metric)) if metric=='BLEU': logging.warning('BLEU is ambiguous, assuming IBM_BLEU\n') metric = 'IBM_BLEU' elif metric=='COMBI': logging.warning('COMBI metric is no longer supported, switching to ' - 'COMB:TER=-0.5;BLEU=0.5\n') + 'COMB:TER=-0.5;BLEU=0.5') metric = 'COMB:TER=-0.5;BLEU=0.5' stats = sum(scorer(r).evaluate(h) for h,r in itertools.izip(hyps,refs)) - logging.info(stats.detail+'\n') + logging.info('Score={} ({})'.format(stats.score, stats.detail)) return stats.score #create new parallel input file in output directory in sgml format @@ -71,6 +82,8 @@ def main(): #set logging to write all info messages to stderr logging.basicConfig(level=logging.INFO) script_dir = os.path.dirname(os.path.abspath(sys.argv[0])) + if not have_mpl: + logging.warning('Failed to import matplotlib, graphs will not be generated.') parser= argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter) @@ -181,10 +194,11 @@ def main(): dev_size = enseg(args.devset, newdev, args.grammar_prefix) args.devset = newdev - write_config(args) + log_config(args) args.weights, hope_best_fear = optimize(args, script_dir, dev_size) - graph_file = graph(args.output_dir, hope_best_fear, args.metric) + graph_file = '' + if have_mpl: graph_file = graph(args.output_dir, hope_best_fear, args.metric) dev_results, dev_bleu = evaluate(args.devset, args.weights, args.config, script_dir, args.output_dir) @@ -205,17 +219,12 @@ def main(): if graph_file: logging.info('A graph of the best/hope/fear scores over the iterations ' - 'has been saved to {}\n'.format(graph_file)) + 'has been saved to {}'.format(graph_file)) print 'final weights:\n{}\n'.format(args.weights) #graph of hope/best/fear metric values across all iterations def graph(output_dir, hope_best_fear, metric): - try: - import matplotlib.pyplot as plt - except ImportError: - logging.error('Error importing matplotlib. Graphing disabled.\n') - return '' max_y = float(max(hope_best_fear['best']))*1.5 plt.plot(hope_best_fear['best'], label='best') plt.plot(hope_best_fear['hope'], label='hope') @@ -308,6 +317,7 @@ def optimize(args, script_dir, dev_size): decoder = script_dir+'/kbest_cut_mira' (source, refs) = split_devset(args.devset, args.output_dir) port = random.randint(15000,50000) + logging.info('using port {}'.format(port)) num_features = 0 last_p_score = 0 best_score_iter = -1 @@ -316,8 +326,8 @@ def optimize(args, script_dir, dev_size): hope_best_fear = {'hope':[],'best':[],'fear':[]} #main optimization loop while i<args.max_iterations: - logging.info('\n\nITERATION {}\n========\n'.format(i)) - logging.info('using port {}\n'.format(port)) + logging.info('======= STARTING ITERATION {} ======='.format(i)) + logging.info('Starting at {}'.format(time.asctime())) #iteration specific files runfile = args.output_dir+'/run.raw.'+str(i) @@ -327,10 +337,8 @@ def optimize(args, script_dir, dev_size): weightdir = args.output_dir+'/weights.pass'+str(i) os.mkdir(logdir) os.mkdir(weightdir) - - logging.info('RUNNING DECODER AT {}'.format(time.asctime())) weightsfile = args.output_dir+'/weights.'+str(i) - logging.info('ITER {}\n'.format(i)) + logging.info(' log directory={}'.format(logdir)) curr_pass = '0{}'.format(i) decoder_cmd = ('{0} -c {1} -w {2} -r{3} -m {4} -s {5} -b {6} -k {7} -o {8}' ' -p {9} -O {10} -D {11} -h {12} -f {13} -C {14}').format( @@ -350,7 +358,7 @@ def optimize(args, script_dir, dev_size): parallelize, logdir, args.jobs) cmd = parallel_cmd + ' ' + decoder_cmd - logging.info('COMMAND: \n{}\n'.format(cmd)) + logging.info('OPTIMIZATION COMMAND: {}'.format(cmd)) dlog = open(decoderlog,'w') runf = open(runfile,'w') @@ -365,27 +373,26 @@ def optimize(args, script_dir, dev_size): p1.stdout.close() if exit_code: - logging.error('Failed with exit code {}\n'.format(exit_code)) + logging.error('Failed with exit code {}'.format(exit_code)) sys.exit(exit_code) try: f = open(runfile) except IOError, msg: - logging.error('Unable to open {}\n'.format(runfile)) + logging.error('Unable to open {}'.format(runfile)) sys.exit() num_topbest = sum(1 for line in f) f.close() if num_topbest == dev_size: break - logging.warning('Incorrect number of top best. ' - 'Waiting for distributed filesystem and retrying.') + logging.warning('Incorrect number of top best. Sleeping for 10 seconds and retrying...') time.sleep(10) retries += 1 if dev_size != num_topbest: logging.error("Dev set contains "+dev_size+" sentences, but we don't " "have topbest for all of these. Decoder failure? " - " Check "+decoderlog+'\n') + " Check "+decoderlog) sys.exit() dlog.close() runf.close() @@ -427,7 +434,7 @@ def optimize(args, script_dir, dev_size): hope_best_fear['hope'].append(dec_score) hope_best_fear['best'].append(dec_score_h) hope_best_fear['fear'].append(dec_score_f) - logging.info('DECODER SCORE: {0} HOPE: {1} FEAR: {2}\n'.format( + logging.info('DECODER SCORE: {0} HOPE: {1} FEAR: {2}'.format( dec_score, dec_score_h, dec_score_f)) if dec_score > best_score: best_score_iter = i @@ -436,12 +443,13 @@ def optimize(args, script_dir, dev_size): new_weights_file = '{}/weights.{}'.format(args.output_dir, i+1) last_weights_file = '{}/weights.{}'.format(args.output_dir, i) i += 1 - weight_files = weightdir+'/weights.mira-pass*.*[0-9].gz' + weight_files = args.output_dir+'/weights.pass*/weights.mira-pass*[0-9].gz' average_weights(new_weights_file, weight_files) - logging.info('\nBEST ITER: {} :: {}\n\n'.format( + logging.info('BEST ITERATION: {} (SCORE={})'.format( best_score_iter, best_score)) weights_final = args.output_dir+'/weights.final' + logging.info('WEIGHTS FILE: {}'.format(weights_final)) shutil.copy(last_weights_file, weights_final) average_final_weights(args.output_dir) @@ -481,15 +489,15 @@ def gzip_file(filename): #average the weights for a given pass def average_weights(new_weights, weight_files): - logging.info('AVERAGE {} {}\n'.format(new_weights, weight_files)) + logging.info('AVERAGE {} {}'.format(new_weights, weight_files)) feature_weights = {} total_mult = 0.0 for path in glob.glob(weight_files): score = gzip.open(path) mult = 0 - logging.info('FILE {}\n'.format(path)) + logging.info(' FILE {}'.format(path)) msg, ran, mult = score.readline().strip().split(' ||| ') - logging.info('Processing {} {}'.format(ran, mult)) + logging.info(' Processing {} {}'.format(ran, mult)) for line in score: f,w = line.split(' ',1) if f in feature_weights: @@ -500,34 +508,30 @@ def average_weights(new_weights, weight_files): score.close() #write new weights to outfile + logging.info('Writing averaged weights to {}'.format(new_weights)) out = open(new_weights, 'w') for f in iter(feature_weights): avg = feature_weights[f]/total_mult - logging.info('{} {} {} ||| Printing {} {}\n'.format(f,feature_weights[f], - total_mult, f, avg)) out.write('{} {}\n'.format(f,avg)) -def write_config(args): - config = ('\n' - 'DECODER: ' - '/usr0/home/eschling/cdec/training/mira/kbest_cut_mira\n' - 'INI FILE: '+args.config+'\n' - 'WORKING DIRECTORY: '+args.output_dir+'\n' - 'DEVSET: '+args.devset+'\n' - 'EVAL METRIC: '+args.metric+'\n' - 'MAX ITERATIONS: '+str(args.max_iterations)+'\n' - 'DECODE NODES: '+str(args.jobs)+'\n' - 'INITIAL WEIGHTS: '+args.weights+'\n') +def log_config(args): + logging.info('WORKING DIRECTORY={}'.format(args.output_dir)) + logging.info('INI FILE={}'.format(args.config)) + logging.info('DEVSET={}'.format(args.devset)) + logging.info('EVAL METRIC={}'.format(args.metric)) + logging.info('MAX ITERATIONS={}'.format(args.max_iterations)) + logging.info('PARALLEL JOBS={}'.format(args.jobs)) + logging.info('INITIAL WEIGHTS={}'.format(args.weights)) if args.grammar_prefix: - config += 'GRAMMAR PREFIX: '+str(args.grammar_prefix)+'\n' + logging.info('GRAMMAR PREFIX={}'.format(args.grammar_prefix)) if args.test: - config += 'TEST SET: '+args.test+'\n' + logging.info('TEST SET={}'.format(args.test)) + else: + logging.info('TEST SET=none specified') if args.test_config: - config += 'TEST CONFIG: '+args.test_config+'\n' + logging.info('TEST CONFIG={}'.format(args.test_config)) if args.email: - config += 'EMAIL: '+args.email+'\n' - - logging.info(config) + logging.info('EMAIL={}'.format(args.email)) if __name__=='__main__': main() diff --git a/training/pro/mr_pro_map.cc b/training/pro/mr_pro_map.cc index eef40b8a..a5e6e48f 100644 --- a/training/pro/mr_pro_map.cc +++ b/training/pro/mr_pro_map.cc @@ -2,7 +2,6 @@ #include <iostream> #include <fstream> #include <vector> -#include <tr1/unordered_map> #include <boost/functional/hash.hpp> #include <boost/shared_ptr.hpp> diff --git a/training/utils/candidate_set.cc b/training/utils/candidate_set.cc index 087efec3..33dae9a3 100644 --- a/training/utils/candidate_set.cc +++ b/training/utils/candidate_set.cc @@ -1,6 +1,11 @@ #include "candidate_set.h" -#include <tr1/unordered_set> +#ifndef HAVE_OLD_CPP +# include <unordered_set> +#else +# include <tr1/unordered_set> +namespace std { using std::tr1::unordered_set; } +#endif #include <boost/functional/hash.hpp> @@ -139,12 +144,12 @@ void CandidateSet::ReadFromFile(const string& file) { void CandidateSet::Dedup() { if(!SILENT) cerr << "Dedup in=" << cs.size(); - tr1::unordered_set<Candidate, CandidateHasher, CandidateCompare> u; + unordered_set<Candidate, CandidateHasher, CandidateCompare> u; while(cs.size() > 0) { u.insert(cs.back()); cs.pop_back(); } - tr1::unordered_set<Candidate, CandidateHasher, CandidateCompare>::iterator it = u.begin(); + unordered_set<Candidate, CandidateHasher, CandidateCompare>::iterator it = u.begin(); while (it != u.end()) { cs.push_back(*it); it = u.erase(it); diff --git a/training/utils/online_optimizer.h b/training/utils/online_optimizer.h index 28d89344..19223e9d 100644 --- a/training/utils/online_optimizer.h +++ b/training/utils/online_optimizer.h @@ -1,10 +1,10 @@ #ifndef _ONL_OPTIMIZE_H_ #define _ONL_OPTIMIZE_H_ -#include <tr1/memory> #include <set> #include <string> #include <cmath> +#include <boost/shared_ptr.hpp> #include "sparse_vector.h" struct LearningRateSchedule { @@ -56,7 +56,7 @@ struct ExponentialDecayLearningRate : public LearningRateSchedule { class OnlineOptimizer { public: virtual ~OnlineOptimizer(); - OnlineOptimizer(const std::tr1::shared_ptr<LearningRateSchedule>& s, + OnlineOptimizer(const boost::shared_ptr<LearningRateSchedule>& s, size_t batch_size, const std::vector<int>& frozen_feats = std::vector<int>()) : N_(batch_size),schedule_(s),k_() { @@ -77,13 +77,13 @@ class OnlineOptimizer { std::set<int> frozen_; // frozen (non-optimizing) features private: - std::tr1::shared_ptr<LearningRateSchedule> schedule_; + boost::shared_ptr<LearningRateSchedule> schedule_; int k_; // iteration count }; class CumulativeL1OnlineOptimizer : public OnlineOptimizer { public: - CumulativeL1OnlineOptimizer(const std::tr1::shared_ptr<LearningRateSchedule>& s, + CumulativeL1OnlineOptimizer(const boost::shared_ptr<LearningRateSchedule>& s, size_t training_instances, double C, const std::vector<int>& frozen) : OnlineOptimizer(s, training_instances, frozen), C_(C), u_() {} diff --git a/training/utils/optimize_test.cc b/training/utils/optimize_test.cc index bff2ca03..72fcef6d 100644 --- a/training/utils/optimize_test.cc +++ b/training/utils/optimize_test.cc @@ -2,6 +2,7 @@ #include <iostream> #include <sstream> #include <boost/program_options/variables_map.hpp> +#include <boost/shared_ptr.hpp> #include "optimize.h" #include "online_optimizer.h" #include "sparse_vector.h" @@ -96,14 +97,11 @@ void TestOptimizerVariants(int num_vars) { cerr << oa.Name() << " SUCCESS\n"; } -using namespace std::tr1; - void TestOnline() { size_t N = 20; double C = 1.0; double eta0 = 0.2; - std::tr1::shared_ptr<LearningRateSchedule> r(new ExponentialDecayLearningRate(N, eta0, 0.85)); - //shared_ptr<LearningRateSchedule> r(new StandardLearningRate(N, eta0)); + boost::shared_ptr<LearningRateSchedule> r(new ExponentialDecayLearningRate(N, eta0, 0.85)); CumulativeL1OnlineOptimizer opt(r, N, C, std::vector<int>()); assert(r->eta(10) < r->eta(1)); } |